where \(e_k\)  corresponds to the current training epoch number.
Here, we introduce a cyclical factor \(f_c\)  that generalizes the shape of the cycle. If \(f_c = 1\) , Equation REF  goes from the \(P_i\)  at the beginning of the training to \(P_m\)  at the end (see Figure REF ). If \(f_c = 2\) , the shape for \(\xi \)  resembles an upside down equilateral triangle (i.e., going from \(P_i\)  to \(P_m\)  in the first half of training and from \(P_m\)  to \(P_i\)  in the second half). If \(f_c = 4, P_m\) , is reached at a quarter of the way through the training and then linearly decreases from \(P_m\)  to \(P_i\)  for the remaining epochs.
Although we don't discuss any other schedule, one can imitate the more complex learning rate schedules, such as stochastic gradient descent with warm restarts (SGDR) [1]} or a polynomial schedule.
